{
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      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<table class=\"ee-notebook-buttons\" align=\"left\">\n",
        "    <td><a target=\"_blank\"  href=\"https://github.com/giswqs/earthengine-py-notebooks/tree/master/Tutorials/Keiko/fire_australia.ipynb\"><img width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /> View source on GitHub</a></td>\n",
        "    <td><a target=\"_blank\"  href=\"https://nbviewer.jupyter.org/github/giswqs/earthengine-py-notebooks/blob/master/Tutorials/Keiko/fire_australia.ipynb\"><img width=26px src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/883px-Jupyter_logo.svg.png\" />Notebook Viewer</a></td>\n",
        "    <td><a target=\"_blank\"  href=\"https://colab.research.google.com/github/giswqs/earthengine-py-notebooks/blob/master/Tutorials/Keiko/fire_australia.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /> Run in Google Colab</a></td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Install Earth Engine API and geemap\n",
        "Install the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geemap](https://github.com/giswqs/geemap). The **geemap** Python package is built upon the [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) and [folium](https://github.com/python-visualization/folium) packages and implements several methods for interacting with Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, and `Map.centerObject()`.\n",
        "The following script checks if the geemap package has been installed. If not, it will install geemap, which automatically installs its [dependencies](https://github.com/giswqs/geemap#dependencies), including earthengine-api, folium, and ipyleaflet.\n",
        "\n",
        "**Important note**: A key difference between folium and ipyleaflet is that ipyleaflet is built upon ipywidgets and allows bidirectional communication between the front-end and the backend enabling the use of the map to capture user input, while folium is meant for displaying static data only ([source](https://blog.jupyter.org/interactive-gis-in-jupyter-with-ipyleaflet-52f9657fa7a)). Note that [Google Colab](https://colab.research.google.com/) currently does not support ipyleaflet ([source](https://github.com/googlecolab/colabtools/issues/60#issuecomment-596225619)). Therefore, if you are using geemap with Google Colab, you should use [`import geemap.eefolium`](https://github.com/giswqs/geemap/blob/master/geemap/eefolium.py). If you are using geemap with [binder](https://mybinder.org/) or a local Jupyter notebook server, you can use [`import geemap`](https://github.com/giswqs/geemap/blob/master/geemap/geemap.py), which provides more functionalities for capturing user input (e.g., mouse-clicking and moving)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Installs geemap package\n",
        "import subprocess\n",
        "\n",
        "try:\n",
        "    import geemap\n",
        "except ImportError:\n",
        "    print('geemap package not installed. Installing ...')\n",
        "    subprocess.check_call([\"python\", '-m', 'pip', 'install', 'geemap'])\n",
        "\n",
        "# Checks whether this notebook is running on Google Colab\n",
        "try:\n",
        "    import google.colab\n",
        "    import geemap.eefolium as geemap\n",
        "except:\n",
        "    import geemap\n",
        "\n",
        "# Authenticates and initializes Earth Engine\n",
        "import ee\n",
        "\n",
        "try:\n",
        "    ee.Initialize()\n",
        "except Exception as e:\n",
        "    ee.Authenticate()\n",
        "    ee.Initialize()  "
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create an interactive map \n",
        "The default basemap is `Google Maps`. [Additional basemaps](https://github.com/giswqs/geemap/blob/master/geemap/basemaps.py) can be added using the `Map.add_basemap()` function. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "Map = geemap.Map(center=[40,-100], zoom=4)\n",
        "Map"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Add Earth Engine Python script "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "# Add Earth Engine dataset\n",
        "# Credits to: Keiko Nomura, Senior Analyst, Space Intelligence Ltd\n",
        "# Source: https://medium.com/google-earth/10-tips-for-becoming-an-earth-engine-expert-b11aad9e598b\n",
        "# GEE JS: https://code.earthengine.google.com/?scriptPath=users%2Fnkeikon%2Fmedium%3Afire_australia \n",
        "\n",
        "geometry = ee.Geometry.Polygon(\n",
        "        [[[153.02512376008724, -28.052192238512877],\n",
        "          [153.02512376008724, -28.702237664294238],\n",
        "          [153.65683762727474, -28.702237664294238],\n",
        "          [153.65683762727474, -28.052192238512877]]])\n",
        "Map.centerObject(ee.FeatureCollection(geometry), 10)\n",
        "\n",
        "# Use clear images from May and Dec 2019\n",
        "imageMay = ee.Image('COPERNICUS/S2_SR/20190506T235259_20190506T235253_T56JNP')\n",
        "imageDec = ee.Image('COPERNICUS/S2_SR/20191202T235239_20191202T235239_T56JNP')\n",
        "\n",
        "Map.addLayer(imageMay, {\n",
        "  'bands': ['B4', 'B3', 'B2'],\n",
        "  'min': 0,\n",
        "  'max': 1800\n",
        "}, 'May 2019 (True colours)')\n",
        "Map.addLayer(imageDec, {\n",
        "  'bands': ['B4', 'B3', 'B2'],\n",
        "  'min': 0,\n",
        "  'max': 1800\n",
        "}, 'Dec 2019 (True colours)')\n",
        "\n",
        "# Compute NDVI and use grey colour for areas with NDVI < 0.8 in May 2019\n",
        "NDVI = imageMay.normalizedDifference(['B8', 'B4']).rename('NDVI')\n",
        "grey = imageMay.mask(NDVI.select('NDVI').lt(0.8))\n",
        "\n",
        "Map.addLayer(grey, {\n",
        "  'bands': ['B3', 'B3', 'B3'],\n",
        "  'min': 0,\n",
        "  'max': 1800,\n",
        "  'gamma': 1.5\n",
        "}, 'grey (base)')\n",
        "\n",
        "# Export as mosaic. Alternatively you can also use blend().\n",
        "mosaicDec = ee.ImageCollection([\n",
        "  imageDec.visualize(**{\n",
        "    'bands': ['B4', 'B3', 'B2'],\n",
        "    'min': 0,\n",
        "    'max': 1800\n",
        "  }),\n",
        "  grey.visualize(**{\n",
        "    'bands': ['B3', 'B3', 'B3'],\n",
        "    'min': 0,\n",
        "    'max': 1800\n",
        "  }),\n",
        "]).mosaic()\n",
        "\n",
        "mosaicMay = ee.ImageCollection([\n",
        "  imageMay.visualize(**{\n",
        "    'bands': ['B4', 'B3', 'B2'],\n",
        "    'min': 0,\n",
        "    'max': 1800\n",
        "  }),\n",
        "  grey.visualize(**{\n",
        "    'bands': ['B3', 'B3', 'B3'],\n",
        "    'min': 0,\n",
        "    'max': 1800\n",
        "  }),\n",
        "]).mosaic()\n",
        "\n",
        "# Export.image.toDrive({\n",
        "#   'image': mosaicMay,\n",
        "#   description: 'May',\n",
        "#   'region': geometry,\n",
        "#   crs: 'EPSG:3857',\n",
        "#   'scale': 10\n",
        "# })\n",
        "\n",
        "# Export.image.toDrive({\n",
        "#   'image': mosaicDec,\n",
        "#   description: 'Dec',\n",
        "#   'region': geometry,\n",
        "#   crs: 'EPSG:3857',\n",
        "#   'scale': 10\n",
        "# })\n",
        "\n",
        "# ============ #\n",
        "#  Topography  #\n",
        "# ============ #\n",
        "\n",
        "# Add topography by computing a hillshade using the terrain algorithms\n",
        "elev = ee.Image('USGS/SRTMGL1_003')\n",
        "shadeAll = ee.Terrain.hillshade(elev)\n",
        "shade = shadeAll.mask(elev.gt(0)) # mask the sea\n",
        "\n",
        "mayTR = ee.ImageCollection([\n",
        "  imageMay.visualize(**{\n",
        "    'bands': ['B4', 'B3', 'B2'],\n",
        "    'min': 0,\n",
        "    'max': 1800\n",
        "  }),\n",
        "  shade.visualize(**{\n",
        "    'bands': ['hillshade', 'hillshade', 'hillshade'],\n",
        "    'opacity': 0.2\n",
        "  }),\n",
        "]).mosaic()\n",
        "\n",
        "highVeg = NDVI.gte(0.8).visualize(**{\n",
        "  'min': 0,\n",
        "  'max': 1\n",
        "})\n",
        "\n",
        "Map.addLayer(mayTR.mask(highVeg), {\n",
        "  'gamma': 0.8\n",
        "}, 'May (with topography)',False)\n",
        "\n",
        "# Convert the visualized elevation to HSV, first converting to [0, 1] data.\n",
        "hsv = mayTR.divide(255).rgbToHsv()\n",
        "# Select only the hue and saturation bands.\n",
        "hs = hsv.select(0, 1)\n",
        "# Convert the hillshade to [0, 1] data, as expected by the HSV algorithm.\n",
        "v = shade.divide(255)\n",
        "# Create a visualization image by converting back to RGB from HSV.\n",
        "# Note the cast to byte in order to export the image correctly.\n",
        "rgb = hs.addBands(v).hsvToRgb().multiply(255).byte()\n",
        "\n",
        "Map.addLayer(rgb.mask(highVeg), {\n",
        "  'gamma': 0.5\n",
        "}, 'May (topography visualised)')\n",
        "\n",
        "# Export the image\n",
        "mayTRMosaic = ee.ImageCollection([\n",
        "  rgb.mask(highVeg).visualize(**{\n",
        "  'gamma': 0.5}),\n",
        "  grey.visualize(**{\n",
        "    'bands': ['B3', 'B3', 'B3'],\n",
        "    'min': 0,\n",
        "    'max': 1800\n",
        "  }),\n",
        "]).mosaic()\n",
        "\n",
        "# Export.image.toDrive({\n",
        "#   'image': mayTRMosaic,\n",
        "#   description: 'MayTerrain',\n",
        "#   'region': geometry,\n",
        "#   crs: 'EPSG:3857',\n",
        "#   'scale': 10\n",
        "# })\n",
        "\n",
        "decTR = ee.ImageCollection([\n",
        "  imageDec.visualize(**{\n",
        "    'bands': ['B4', 'B3', 'B2'],\n",
        "    'min': 0,\n",
        "    'max': 1800\n",
        "  }),\n",
        "  shade.visualize(**{\n",
        "    'bands': ['hillshade', 'hillshade', 'hillshade'],\n",
        "    'opacity': 0.2\n",
        "  }),\n",
        "]).mosaic()\n",
        "\n",
        "Map.addLayer(decTR.mask(highVeg), {\n",
        "  'gamma': 0.8\n",
        "}, 'Dec (with topography)',False)\n",
        "\n",
        "# Convert the visualized elevation to HSV, first converting to [0, 1] data.\n",
        "hsv = decTR.divide(255).rgbToHsv()\n",
        "# Select only the hue and saturation bands.\n",
        "hs = hsv.select(0, 1)\n",
        "# Convert the hillshade to [0, 1] data, as expected by the HSV algorithm.\n",
        "v = shade.divide(255)\n",
        "# Create a visualization image by converting back to RGB from HSV.\n",
        "# Note the cast to byte in order to export the image correctly.\n",
        "rgb = hs.addBands(v).hsvToRgb().multiply(255).byte()\n",
        "\n",
        "Map.addLayer(rgb.mask(highVeg), {\n",
        "  'gamma': 0.5\n",
        "}, 'Dec (topography visualised)')\n",
        "\n",
        "# Export the image\n",
        "decTRMosaic = ee.ImageCollection([\n",
        "  rgb.mask(highVeg).visualize(**{\n",
        "    'gamma': 0.5\n",
        "  }),\n",
        "  grey.visualize(**{\n",
        "    'bands': ['B3', 'B3', 'B3'],\n",
        "    'min': 0,\n",
        "    'max': 1800\n",
        "  }),\n",
        "]).mosaic()\n",
        "\n",
        "# Export.image.toDrive({\n",
        "#   'image': decTRMosaic,\n",
        "#   description: 'DecTerrain',\n",
        "#   'region': geometry,\n",
        "#   crs: 'EPSG:3857',\n",
        "#   'scale': 10\n",
        "# })\n"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Display Earth Engine data layers "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "Map.addLayerControl() # This line is not needed for ipyleaflet-based Map.\n",
        "Map"
      ],
      "outputs": [],
      "execution_count": null
    }
  ],
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